Oversampled Adaptive Sensing
Müller R, Bereyhi A, Mecklenbräuker CF (2018)
Publication Language: English
Publication Type: Conference contribution, Original article
Publication year: 2018
Publisher: IEEE
Event location: San Diego
Open Access Link: https://arxiv.org/abs/1802.03056
Abstract
We develop a Bayesian framework for sensing which adapts the sensing time
and/or basis functions to the instantaneous sensing quality measured in terms
of the expected posterior mean-squared error. For sparse Gaussian sources a
significant reduction in average sensing time and/or mean-squared error is
achieved in comparison to non-adaptive sensing. For compression ratio 3, a
sparse 10% Gaussian source and equal average sensing times, the proposed method
gains about 2 dB over the performance bound of optimum compressive sensing,
about 3 dB over non-adaptive 3-fold oversampled orthogonal sensing and about 6
to 7 dB to LASSO-based recovery schemes while enjoying polynomial time
complexity.
We utilize that in the presence of Gaussian noise the mean-squared error
conditioned on the current observation is proportional to the derivative of the
conditional mean estimate with respect to this observation.
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Involved external institutions
Austria (AT)How to cite
APA:
Müller, R., Bereyhi, A., & Mecklenbräuker, C.F. (2018). Oversampled Adaptive Sensing. In Proceedings of the Information Theory and Applications Workshop (ITA). San Diego, US: IEEE.
MLA:
Müller, Ralf, Ali Bereyhi, and Christoph F. Mecklenbräuker. "Oversampled Adaptive Sensing." Proceedings of the Information Theory and Applications Workshop (ITA), San Diego IEEE, 2018.
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